The qualitative results obtained by the proposed deep attention network,
along with that of other recent methods on a few example
images from MIT1003 [1]}, TORONTO [2]},
PASCAL-S [3]} and DUT-OMRON [4]}
datasets are presented in Fig. REF . As shown in
the figures, the proposed attention model is able to consistently
capture saliency arising from both low-level features such as colour contrast as well as
the more high-level aspects such
as humans, faces and text. Our saliency maps are very localized
in the salient regions compared with other methods, even when images
have cluttered backgrounds or salient regions in different sizes. We attribute the performance
of the proposed deep attention model to its large depth and the utilization of multi-level features.
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